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Research Article Section: Marine Sciences

Simulation Analysis on Genomic Selection of Grouper (Epinephelus coioides) Breeding for Categorical Traits

Author(s): Zhiyuan Ma and Xinxin You*

Volume 1, Issue 1, 2021

Published on: 09 September, 2020

Page: [87 - 97] Pages: 11

DOI: 10.2174/2210298101999200909111243

Abstract

Background: The basic principle of genome selection (GS) is to establish a model of genome estimated breeding value (GEBV) by using single-nucleotide polymorphisms (SNPs) covering the entire genome. Despite the decreasing cost of high-throughput genotyping, the GS strategy remains expensive due to the need for phenotyping and genotyping for a large number of samples. Simulation analysis of genome selection is a popular, lower-cost method to determine an optimal breeding program of GS.

Objective: To evaluate the utility of simulation data to study the influence of different factors on algorithms. This could be helpful for developing genome selection breeding strategies, especially for stress and resistance traits of fish.

Methods: Real data of orange-spotted grouper (Epinephelus coioides) were obtained from a previous genome-wide association study. Ammonia tolerance, different population sizes, SNP density, QTL number, kinship (base mutation rate), and heritability were considered. All of the phenotypes and genotypes were generated by AlphaSimR simulation software. Four genome selection algorithms (gBLUP, rrBLUP, BayesA, and BayesC) were tested to derive GEBV, and their accuracies (area under the curve, AUC) were compared.

Results: In different scenarios, the AUC ranges from 0.4237 to 0.6895 for BayesA, 0.4282 to 0.6878 for BayesC, 0.4278 to 0.6798 for gBLUP, and 0.4346 to 0.6834 for rrBLUP. The mean AUC of these four algorithms was not significantly different (0.547–0.548). The accuracies of the four genome selection algorithms were similar but had different predictive performances in specific scenarios. The gBLUP was most stable, and the rrBLUP was slightly better at predicting low heritability traits. When the number of individuals was small, the BayesA and BayesC algorithms were more robust.

Conclusion: A practical GS scheme should be optimized in accordance with marker density, heritability, and reference population size. Adequate preliminary research is necessary. The results provide a framework for the design of genomic selection schemes in E. coioides breeding.

Keywords: gBLUP, rrBLUP, BayesA, BayesC, categorical traits, grouper, genomic selection.

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